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Seasonal variation in objectively measured physical activity, sedentary time, cardiorespiratory fitness and sleep duration among 8–11 year-old Danish children: a
repeated-measures study
Hjorth, Mads F.; Chaput, Jean-Philippe; Michaelsen, Kim; Astrup, Arne; Tetens, Inge; Sjödin, Anders
Published in:
B M C Public Health
DOI:
10.1186/1471-2458-13-808
Publication date:
2013
Document Version
Publisher final version (usually the publisher pdf)
Link to publication
Citation (APA):
Hjorth, M. F., Chaput, J-P., Michaelsen, K., Astrup, A., Tetens, I., & Sjödin, A. (2013). Seasonal variation in
objectively measured physical activity, sedentary time, cardio-respiratory fitness and sleep duration among 8–11
year-old Danish children: a repeated-measures study. B M C Public Health, 13(1), [808]. 10.1186/1471-2458-13808
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Hjorth et al. BMC Public Health 2013, 13:808
http://www.biomedcentral.com/1471-2458/13/808
RESEARCH ARTICLE
Open Access
Seasonal variation in objectively measured
physical activity, sedentary time, cardio-respiratory
fitness and sleep duration among 8–11 year-old
Danish children: a repeated-measures study
Mads F Hjorth1*, Jean-Philippe Chaput2, Kim Michaelsen1, Arne Astrup1, Inge Tetens3 and Anders Sjödin1
Abstract
Background: Understanding fluctuations in lifestyle indicators is important to identify relevant time periods to
intervene in order to promote a healthy lifestyle; however, objective assessment of multiple lifestyle indicators has
never been done using a repeated-measures design. The primary aim was, therefore, to examine between-season
and within-week variation in physical activity, sedentary behaviour, cardio-respiratory fitness and sleep duration
among 8–11 year-old children.
Methods: A total of 1021 children from nine Danish schools were invited to participate and 834 accepted. Due to
missing data, 730 children were included in the current analytical sample. An accelerometer was worn for 7 days
and 8 nights during autumn, winter and spring, from which physical activity, sedentary time and sleep duration
were measured. Cardio-respiratory fitness was assessed using a 10-min intermittent running test.
Results: The children had 5% more sedentary time, 23% less time in moderate-to-vigorous physical activity
and 2% longer sleep duration during winter compared to spring and cardio-respiratory fitness was 4% higher
during spring compared to autumn (P < 0.001). Sedentary time was higher and total physical activity,
moderate-to-vigorous physical activity and sleep duration (boys only) were lower during weekends at all
seasons (P ≤ 0.01). Intraclass correlation coefficients between seasons ranged from 0.47-0.74, leaving 45-78%
to seasonal variation.
Conclusions: Overall, sedentary time was higher and physical activity lower during winter and during
weekends. The most accurate and unbiased estimates of physical activity came from autumn; however, the
considerable intra-individual variation suggests that a single measurement may not adequately characterise
children’s habitual sleep and activity.
Keywords: Physical activity, Sedentary behaviour, Cardio-respiratory fitness, Sleep duration, Children, Season
Background
The high prevalence of overweight and obesity among
children in most parts of the world is a well-documented
fact. Lower cardio-respiratory fitness (CRF) [1], short sleep
duration [2], low levels of physical activity and high levels
of sedentary behaviour [3] have been linked to the
* Correspondence: [email protected]
1
Department of Nutrition, Exercise and Sports, Faculty of Science, University
of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Copenhagen,
Denmark
Full list of author information is available at the end of the article
development of obesity and cardio-metabolic risk. As the
reliability and validity of estimates of habitual physical
activity [4] and sleep duration [5] is questionable when
self-reported using questionnaires, accelerometers have
become the preferred method of choice to measure physical
activity and sedentary time; in recent years they have
become an important method in sleep research [6].
Although the use of objective methods has increased in
recent years, the majority of studies rely on single point
measurements to characterise habitual physical activity,
sedentary time, sleep duration and CRF. To the best of
© 2013 Hjorth et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication
waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise
stated.
Hjorth et al. BMC Public Health 2013, 13:808
http://www.biomedcentral.com/1471-2458/13/808
our knowledge, none have examined seasonal variation
in accelerometer determined sleep duration or sedentary
behaviour of children using a repeated-measures design,
and only few studies have examined seasonal variation
in physical activity of children using a repeated-measures
design [7-10].
Collectively, objective assessment of seasonal pattern in
multiple lifestyle indicators such as sleep, physical activity
and sedentary behaviour across the year has never been
done using a repeated-measures design. Given that the
weather changes considerably across the year in northern
Europe, it could help to put existing literature into context
and identify relevant time periods to intervene in order to
promote a healthy lifestyle. Furthermore, a single measure
of physical activity, from a random time during the year,
to quantify a relationship with obesity or cardio-metabolic
risk, can lead to random error if large differences exist
between seasons. However, when repeated measurements
of the exposure variable (e.g. physical activity) are collected,
the intra-class correlation coefficient (ICC), a measure
of reliability, can be calculated and used to adjust for
this random error, hence obtaining an improved estimate
of the relationship with health outcomes [11]. Finally,
accuracy and bias of the lifestyle indicators could be
assessed in order to recommend the best season to take a
given measure [12].
The primary aim of this observational study was to
examine between-season and within-week variation in
physical activity, sedentary behaviour, CRF and sleep
duration in a large group of 8–11 year-old Danish school
children. Additionally, we report the level of these lifestyle
indicators, identify correlates of the lifestyle indicators and
investigate the connection between them.
Methods
Study population
The initial sample comprised 834 of the 1021 invited 3rd
and 4th grade students (8–11 years-old) from the nine
representative Danish municipal schools enrolled in the
OPUS (Optimal well-being, development and health for
Danish children through a healthy New Nordic Diet) school
meal study running from August 2011 to June 2012.
The two primary outcomes of the OPUS project were to
investigate concentration performance and metabolic health
of a new Nordic diet served in school [13]. It is a clusterrandomised cross-over study with a number of measurements performed at baseline, at month 3 by the end of the
first dietary period, and at month 6 by the end of the
second dietary period. The nine schools were included from
late August to November 2011. The three measurements
separated by three months are used in the current paper,
representing autumn (late August to November 2011),
winter (November 2011 to February 2012) and spring
(March to beginning of June 2012). The mean (mean of
Page 2 of 10
daily minimum and maximum) temperature [10.2 (7.2;13.0),
2.0 (−0.6;4.2) and 8.0 (4.5;11.7)°C], total rainfall (172, 208
and 112 mm) and sunshine (301, 229 and 575 hours)
changed during autumn, winter and spring, respectively
[14]. Furthermore, daylight increased by more than 10 hours
from December to June. Children were excluded from
the current analytical sample if they did not have valid
measurements from all three seasons in either physical
activity (≥10 hours of measured monitor wear time for
at least 3 weekdays and 1 weekend day), CRF or sleep
duration (at least 3 weekdays and 1 weekend day); hence,
730 children were included. The study was approved by
The Committees on Biomedical Research Ethics for the
Capital region of Denmark and written informed parental
consent of both custody holders and child assent were
obtained for all participants. The study was registered in
the database www.clinicaltrials.gov (no. NCT01457794).
Physical activity, sedentary time and sleep assessment
The children were asked to wear an ActiGraph™ accelerometer (GT3X + or GT3X) tightly at the right hip on an elastic
belt for 7 consecutive days and 8 nights, and to remove it
only during water activities (i.e. showering or swimming).
At the end of the observation period, data were reintegrated
to 60-second epochs and analysed using ActiLife6 (The
ActiGraph 2012, ActiLife version 6).
Before analysis of physical activity and sedentary time
we removed 1) data between midnight and 6 am as this
was expected to be non-awake time, 2) periods of at least
15 minutes of consecutive zero counts using tri-axial vector
magnitudes to remove non-wear time and non-awake
time and 3) consecutive wear time periods of less than
60 minutes to remove non-awake time as sleep for most
children is characterised by minor periods of movement
that we did not want to include in our analysis of physical
activity and sedentary time. Total physical activity (counts/
min; cpm) was expressed as total vertical counts from
wear-time, divided by measured wear-time. Time spent
sedentarily was defined as all minutes showing 100 cpm or
less, which is a widely used cut-off point [15]. Furthermore,
moderate-to-vigorous physical activity (MVPA) was defined as ≥2296 cpm, which is a recently suggested pediatric
cut-off point [15,16]. The number of children fulfilling the
WHO recommendations of an average of ≥ 60 minutes
MVPA/day [17] was also calculated. Weekly average of
physical activity and sedentary time are calculated in the
proportion 5 to 2 between weekdays (Monday-Friday) and
weekend days (Saturday and Sunday).
The parents and children were instructed to keep logs
for bedtimes (lights off and trying to sleep), and waking
time (lights on) during the week in which the monitor
was worn. To estimate accelerometer determined sleep
duration, self-reported bedtimes and waking times were
used as the possible window of sleep and accelerometer
Hjorth et al. BMC Public Health 2013, 13:808
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data within this window were scored in ActiLife6 using
the algorithm by Sadeh et al. [18]. Self-reported sleep
logs were missing for 4% (63 out of 1569) of the sleep
measurements used in the current paper; in these cases,
bedtimes and waking time were visually determined from
the individual actograms. In a random subsample of 105
individuals we found the mean difference in sleep duration
between these two approaches to be small (3.8 min,
P < 0.001). The weekly average of sleep duration is calculated in the proportion 5 to 2 between weekdays (SundayThursday) and weekend days (Friday and Saturday). In total,
the accelerometer was worn for a median (interquartile
range) duration of 21 (20–21) days and 23 (22–24)
nights with at mean ± standard deviation (SD) wear-time
of 900 ± 28 min/day between wake up time and bedtime.
Cardio-respiratory fitness
Children ran as fast as possible between two parallel
lines separated by 20 m for 15 sec following 15 sec of
rest. This procedure was followed for 10 min and the
total distance travelled was used to estimate the maximal
oxygen uptake (VO2max) using an age-specific equation
[19]: VO2max(ml/kg/min) = 18.17 + (0.03301 × distance)–
(5.53 × gender) (boy = 0; girl = 1). Validity and reliability of
this Andersen test were r = 0.64 and r = 0.84, respectively
[19]. The test was performed at the schools by the physical
education teachers and was supervised by scientific staff
during the first of the three tests.
Anthropometric measurements
Barefooted and wearing light clothes, the children were
weighed to the nearest 0.1 kg (Tanita BWB-800S, Tokyo,
Japan) and their heights were measured three times to the
nearest 0.1 cm (average used) (CMS Weighing Equipment
LTD, London, UK). BMI Z-score was calculated based on
the WHO Growth Reference from 2007 [20] and the
prevalence of underweight, normal weight, overweight
and obese children was calculated based on age- and
gender-specific cut-offs defined to pass through body mass
index at 18.5, 25 and 30 kg/m2 at age 18 years [21,22].
Questionnaire data
A baseline questionnaire ascertained age, gender, grade,
highest education of household (divided into four groups
according to years of education: ≤10 years, 11–12 years,
13–16 years, ≥17 years), number of parents born in
Denmark (a proxy for ethnicity), and screen time. Screen
time was computed based on the parent-reported time
spent watching television, playing non-active video games
or using the computer for leisure on weekdays and weekend days. Playing Nintendo Wii or similar active video
game devices was not included in screen time. The weekly
average of screen time was calculated in the proportion 5
to 2 between weekdays and weekend days. The number of
Page 3 of 10
children fulfilling the recommendations of less than two
hours of screen time/day [23] was also calculated. Pubertal
status was reported by parents and children in collaboration on the basis of breast development among girls and
pubic hair among boys [24].
Statistical analysis
Descriptive characteristics of the study sample are presented
as mean ± SD or as proportions (%) and gender differences
tested using a two-sample t-test or Pearson’s chi-squared
test. A two-way ANOVA was carried out to investigate the
effect of the diet intervention and season on all variables of
interest. As no effect of the diet intervention was found in
any of the variables (P > 0.33) the control- and intervention
groups from the original study were combined for this
study to maximize power. A one-way ANOVA with
Bonferroni adjustments was carried out to investigate the
effect of season, and ICC were calculated. To address the
issue of accuracy, the mean absolute percentage error
(MAPE) and bias were calculated for all lifestyle indicators
between each season and average of the three measurements obtained during the different seasons as presented
elsewhere [12]. To investigate the potential difference
between weekdays and weekend days and the weekend
day/weekday-ratio between seasons a paired t-test and
Pearson’s chi-squared test was used. Non-normally distributed data were presented as median (interquartile range)
and square-root-transformed before testing. The overall
effect between variables of interest and season or weekdays/weekend days were not affected by gender (except
sleep duration between weekdays and weekend days) and
data for boys and girls were therefore combined. Univariate regression analysis was used to identify correlates of
MVPA, sedentary time, CRF and sleep duration. Data
are presented as mean and 95% confidence interval (CI).
Linearity between residuals and the dependent variables
in the model were visually checked along with normal
distribution of the residuals and homogeneity of variance
of the residuals. Partial correlation coefficients (adjusting
for age, grade, gender and pubertal status) were performed
between several lifestyle indicators. The level of significance
was set at P < 0.05 and analyses were done using STATA/
IC 11.2 (Houston, USA).
Results
Descriptive characteristics of the participants are presented
in Table 1. Overall, boys were slightly older than girls
(P = 0.001) and more girls than boys had entered puberty
(P < 0.001). A total of 13.3% of the children were classified
as overweight or obese. As shown in Table 2, the children
had 5% more sedentary time, 23% less time in MVPA and
2% longer sleep duration during winter compared to
spring and CRF was 4% higher during spring compared to
autumn (P < 0.001). Except from sedentary time, bias and
Hjorth et al. BMC Public Health 2013, 13:808
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Table 1 Descriptive characteristics of the study population by gender
Variable
Boys (n = 376)
Girls (n = 354)
All (n = 730)
Age (years)
10.0 ± 0.6
9.9 ± 0.6 *
10.0 ± 0.6
3rd grade/4th grade
49% / 51%
49% / 51%
49% / 51%
Pubertal status (% 1/2/≥3)
76.5 / 19.4 / 4.2
54.5 / 36.2 / 9.3 **
65.7 / 27.6 / 6.7
Parents born in Denmark (% 0 / 1 / 2)
11.2 / 12.5 / 76.3
10.5 / 11.9 / 77.7
10.8 / 12.2 / 77.0
Highest education of household (%)1
4.5 / 31.7 / 40.5 / 23.2
5.9 / 30.9 / 40.5 / 22.7
5.2 / 31.3 / 40.5 / 22.9
Weight (kg)
35.5 ± 7.2
34.5 ± 6.7
35.0 ± 7.0
Height (cm)
142.6 ± 7.1
142.1 ± 7.0
142.4 ± 7.1
Body mass index Z-score2
0.23 ± 1.12
0.04 ± 1.03 *
0.13 ± 1.08
Weight status (% uw/nw/ow/ob)3
8.3/77.8/11.5/2.4
12.3/75.1/11.4/1.1
10.2/76.5/11.5/1.8
Data are presented as mean ± standard deviation or proportions (%) and gender differences were tested using a two-sample t-test or Pearson’s chi-squared test.
*, P < 0.05; **, P < 0.001.
1
Highest education of household: ≤10 years / 11–12 years / 13–16 years / ≥17 years.
2
Based on World Health Organization Growth Reference data from 2007 [20].
3
Based on age- and gender-specific cut-offs defined to pass through body mass index at 18.5, 25 and 30 kg/m2 at age 18 years [21,22]; uw/nw/ow/ob,
underweight/normal weight/overweight/obese.
MAPE were largest during spring. No differences were
found in the variables measured at the first occasion
with the inclusion criteria of having all three measurements (P > 0.21), except for sleep duration, which was 8
(95%CI 4–12) min less for the 247 children who were
only measured at the first visit (P < 0.001).
As shown in Table 3 sedentary time and screen-time
were higher and total physical activity, MVPA and sleep
duration (only boys) were lower during weekends at all
measurement occasions (P ≤ 0.01). The weekend day/
weekday-ratio of total physical activity and MVPA was
lower during winter compared to spring (P < 0.001).
As shown in Table 4, an increase in age of 1 year was
associated with a decrease in MVPA (2.7 min/day) and
sleep duration (10.2 min/night) as well as an increase in
sedentary time (27.8 min/day) and CRF (0.8 ml/kg/min)
(P < 0.05). The same pattern was seen for the difference
between 3rd and 4th graders, except for CRF, with 4th
graders accumulating 5.4 min/day less MVPA, being
32.4 min/day more sedentary and sleeping for 13.6 min
less (P < 0.001). Children who had entered puberty
(Tanner stage ≥2) had 7.5 min less MVPA, 12.0 min/day
more sedentary time, 7.2 min/night less sleep and a
2.5 ml/kg/min lower CRF compared to those who had not
(Tanner stage =1) (P < 0.05), while boys had 19.9 min/day
more MVPA, 5.1 min/night less sleep and an 8.0 ml/kg/
min higher CRF compared to girls (P < 0.05). Children
with two parents born outside Denmark had 19.2 min/
night less sleep compared to children having both parents
born in Denmark (P < 0.05). Children whose parents
Table 2 Physical activity, sedentary behaviour, sleep duration and cardio-respiratory fitness according to season
of measurement in 8–11 year old Danish children
Variables
n
Autumn
Winter
Spring
ICC
Bias (%)
MAPE (%)
Sedentary time (min/d)
635
473 ± 63a
486 ± 59b
465 ± 66a **
0.60
−0.4/2.5/-2.1
5.4/5.5/5.8
Screen-time (min/d)1
704
146 (102;193)
Screen-time (≤2 h/d)1,2
704
34%
Total PA (cpm)
635
490 ± 135a
447 ± 120b
557 ± 175c **
0.47
−1.2/-9.9/11.1
11.6a/13.1b/15.1c **
0.64
−2.0/-11.1/13.1
17.9a/19.4ab/21.1b *
MVPA (min/d)
MVPA (≥1 h/d) (%)3
CRF (ml/kg/min)
4
Sleep duration (min/night)
a
635
49 ± 23
635
28%
b
c
44 ± 22
57 ± 27 **
22%
39% **
492
47.5 ± 5.1
48.3 ± 5.4
49.2 ± 5.8c **
0.74
−1.6/-0.1/1.7
3.7a/3.2b/3.9a *
523
556 ± 27a
553 ± 28a
541 ± 30b **
0.50
1.2/0.5/-1.6
2.3a/2.2a/2.7b **
a
ab
Abbreviations: ICC Intraclass correlation coefficient, MAPE Mean absolute percentage error, PA Physical activity, MVPA Moderate-to-vigorous physical activity, CRF
Cardio-respiratory fitness.
Data are presented as mean ± standard deviation or median (interquartile range) tested using one-way ANOVA with post-hoc Bonferroni test (non-normal
distributed data was transformed before analysis) or proportions (%) tested using Pearson’s chi-squared test.
*, P < 0.05; **, P < 0.001. Different superscripts in a row (a,b,c) indicate significant differences (P <0.05).
1
Only measured at baseline.
2
Screen time recommendations [23].
3
Physical activity recommendations [17].
4
Distance run in the CRF-test at autumn, winter and spring were 973, 996 and 1023 meters, respectively.
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Table 3 Physical activity, sedentary behaviour, sleep duration and cardio-respiratory fitness at weekdays and weekend
days at three seasons in 8–11 year old Danish children
Variables
n
Autumn
Weekday
Sedentary time
(min/d)
635 467 ± 66
Winter
Spring
Weekend
Ratio5 Weekday
Weekend
Ratio5 Weekday
Weekend
Ratio5
488 ± 88**
1.05
477 ± 61
509 ± 82**
1.07
481 ± 94**
1.06
459 ± 67
Screen-time (min/d)1 704 120 (80;165)
210 (135;300)**
Screen-time
(≤2 h/d)1,2
704 46%
17%**
Total PA (cpm)
635 507 (417;614) 400 (295;514)**
0.84a
464 (388;566) 347 (259;441)** 0.78b
635 50 (34;71)
28 (16;47)**
0.68a
46 (32;66)
24 (12;39)**
0.63a
56 (38;78)
40 (21;62)**
0.83b **
MVPA (≥1 h/d)
635 37%
15%**
0.41
33%
9%**
0.28
45%
27%**
0.60 *
Sleep duration
(min/night)4
523 559 ± 28
549 ± 48**
0.99
554 ± 31
549 ± 48*
1.00
543 ± 32
536 ± 55*
0.99
MVPA (min/d)
3
555 (450;671) 460 (331;635)** 0.92c **
Abbreviations: PA Physical activity, MVPA Moderate-to-vigorous physical activity.
Data are presented as mean ± standard deviation or median (interquartile range) tested by student t-test or one-way ANOVA with post-hoc Bonferroni test
(non-normal distributed data were transformed before analysis) or proportions (%) tested by Chi-square test. *, P < 0.05; **, P < 0.001. Different superscripts in a
row (a,b,c) indicate significant difference (P ≤0.05).
1
Only measured at baseline.
2
Screen time recommendations [23].
3
Physical activity recommendations [17].
4
Boys were sleeping 12, 11 and 13 min/night less during weekend days compared to weekdays in autumn, winter and spring (P < 0.001). Girls were not sleeping
less during weekend days compared to weekdays (3, -4 and 0 min/night in autumn, winter and spring (P ≥ 0.22).
5
Weekend day/weekday-ratio within season.
Table 4 Associations between average of the three seasons of moderate-to-vigorous physical activity, sedentary time,
sleep duration and cardio-respiratory fitness with potential explaining variables in 8–11 year old Danish children
Variable1
n
Age (years)
730
Grade
730
Moderate-to-vigorous
physical activity
(min/day) (n = 635)
P
B (95%CI)
P
B (95%CI)
P
B (95%CI)
p
0.0463
27.8 (21.5;34.1)
<0.001
−10.2 (−13.4;-7.1)
<0.001
0.8 (0.1;1.5)
0.03
Reference
4th graders
371
−5.4 (−8.7;-2.1)
464
Reference
≥2
242
−7.5 (−11.0;-3.9)
Gender
730
Girls
354
Reference
376
19.9 (16.9;22.9)
79
Reference
1
89
2.3 (−4.8;9.4)
562
−2.4 (−8.0;3.2)
2
1
<0.001
32.4 (24.4;40.4)
Reference
<0.001
−13.6 (−17.5;-9.6)
0.01
−7.2 (−11.6;-2.9)
Reference
<0.001
−0.1 (−1.0;0.8)
0.001
−2.5 (−3.4;-1.6)
0.87
Reference
<0.001
12.0 (3.0;21.0)
Reference
Reference
<0.001
−0.3 (−8.7;8.1)
Reference
Reference
0.95
−5.1 (−9.2;-1.0)
0.16
<0.001
Reference
0.01
8.0 (7.5;8.5)
<0.001
20.8 (11.2;30.5)
<0.001
1.3 (−0.7;3.2)
0.20
0.06
19.2 (11.1;30.5)
<0.001
0.1 (−1.5;1.7)
0.89
730
0
Highest education of parents2
Reference
706
1
Boys
Cardio-respiratory
fitness (ml/kg/min)
(n = 492)
−2.7 (−4.5;-0.8)
359
Parents born in Denmark2
Sleep duration
(min/night) (n = 523)
B (95%CI)
3rd graders
Pubertal status
Sedentary time
(min/day) (n = 635)
Reference
0.52
0.40
12.6 (−5.2;30.4)
13.5 (−0.4;27.4)
Reference
4
Reference
728
≤10 years
38
Reference
11 - 12 years
228
−3.1 (−11.6;5.3)
0.46
13.0 (−7.9;34.0)
0.22
24.4 (12.9;35.9)
<0.001
0.8 (−1.3;3.0)
0.45
13 - 16 years
295
−4.0 (−12.2;4.3)
0.35
22.5 (1.9;43.1)
0.03
26.0 (14.7;37.4)
<0.001
1.1 (−1.1;3.3)
0.31
≥17 years
167
−1.3 (−9.9;7.2)
0.76
24.0 (2.7;45.3)
0.03
23.0 (11.4;34.6)
<0.001
1.5 (−0.7;3.7)
0.20
Reference
Reference
All dependent variables are from the baseline assessments.
2
P > 0.05 between non-reference groups.
3
B = −2.4 (95%CI = −5.8;1.0, P = 0.17) among children with a valid cardio-respiratory fitness test (n = 413).
4
B = 16.6 (95%CI = 3.4;29.9, P = 0.01) if wear-time was included as covariate.
Reference
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Page 6 of 10
had the lowest education (≤10 years) were sleeping for
approximately 24 min less compared to all other educational groups, and were roughly 24 min less sedentary
than children whose parents had an education of ≥13 years
(P < 0.05). Adjusting wear time for sedentary time, total
physical activity and MVPA or expressing sedentary time
and MVPA as percentage of wear time did not change the
results from the regression analysis (except for sedentary
time that was 16.6 min/day lower in children having two
parents born outside Denmark compared to no parents
born outside Denmark).
Partial correlations were carried out between physical
activity, sedentary time, screen time, sleep duration and
CRF, with adjustment for age, grade, gender and pubertal
status, from baseline and from an average of the three
seasons (Table 5). Partial correlation coefficients numerically increased between CRF and each of sedentary time,
total physical activity and MVPA when using the average
of the three seasons compared to baseline only. Moderate
to strong correlations (0.49-0.91) were seen between
sedentary time, total physical activity and MVPA, with
MVPA explaining 82.8% of the variance in total physical
activity. Sleep was negatively correlated with screen
time (r = −0.15, P = 0.001), total physical activity (r = −0.15,
P = 0.001) and MVPA (r = −0.18, P < 0.001), but not with
sedentary time. Screen time was also negatively correlated
with total physical activity (r = −0.11, P = 0.01) and CRF
(r = −0.16, P < 0.001), but positively correlated with sedentary time (r = 0.17, P < 0.001).
Discussion
Overall, sedentary time was higher and physical activity,
including the number of children fulfilling the physical
activity recommendations, was lower during winter and
during weekends. Sleep duration was shorter during spring
and among boys also during weekends. Younger boys who
had not entered puberty were more physically active,
less sedentary and had higher CRF, whereas having two
parents born outside Denmark or with a low education
were associated with shorter sleep but also with less time
spent on sedentary activities.
Level of physical activity, sedentary behaviour,
sleep and fitness
The higher CRF among boys compared to girls was more
pronounced than previously reported from a comparable
Danish population using the same fitness test protocol
[25], but similar to children at the same age from a large
European study using a cycle test [1]. For most individuals
(ranging from 61 to 78% in the different seasons) MVPA
was lower than recommended, which is comparable to
other recent studies using 60 seconds epochs despite
small methodological differences (e.g. cut-off and wear
time criteria) [26,27]. Boys had a 50% higher MVPA
compared to girls, which could have contributed to the
large gender difference in CRF. Sedentary time was two
hours longer than recently reported in a pooled analysis of
more than 20,000 4–18 year old children [3]. The fact that
we used a 24-hour accelerometer protocol is likely to have
contributed to the relatively high amount of time spent in
a sedentary state and relatively low amount of physical
activity compared to other studies. A recent large European
study, however, found an even higher amount of time spent
sedentary and lower physical activity in an adolescent
population without using a 24-hour accelerometer protocol
[27]. Screen time use was high in the present study, with
the majority exceeding the recommendations (66%). Screen
time was less than previously reported in a study from
Canada [28], but comparable to earlier studies from
Denmark [29]. According to the National Sleep Foundation, school-aged children between 5 and 12 years need a
minimum of 10 hours of sleep per night [30]. The reported
“time in bed” in the present study was 10 hours (data not
shown), and other studies using parent or self-reported
sleep duration measures have similar findings [31,32].
Using the objective measurement of sleep, however, sleep
Table 5 Partial correlation coefficients (r) between sedentary time, screen time, physical activity, cardio-respiratory
fitness and sleep duration from baseline and average of the three seasons
Sedentary time
(min/day)
Screen time
(min/day)
Total
PA (cpm)
MVPA
(min/day)
Screen time (min/day)1
0.18 / 0.17
Total PA (cpm)
−0.70 / -0.69
−0.13 / -0.11
MVPA (min/day)
−0.49 / -0.49
−0.08 / -0.06
0.89 / 0.91
CRF (ml/kg/day)
−0.05 / -0.172
−0.14 / -0.16
0.22 / 0.45
0.29 / 0.50
Sleep duration (min/night)
0.06 / 0.05
−0.14 / -0.15
−0.13 / -0.15
−0.15 / -0.18
CRF
(ml/kg/day)
0.02 / -0.05
Abbreviations: PA Physical activity, MVPA Moderate-to-vigorous physical activity, CRF Cardio-respiratory fitness.
First number is partial correlation coefficients (r) from baseline measurements only. Second number is r from the average of all three seasons. Both are controlled
for age, grade, gender and pubertal status.
r ≥ |0.09|: P < 0.05.
r ≥ |0.16|: P < 0.001.
1
Screen time is only measured at baseline.
2
When adjusted for MVPA: r = 0.08; p = 0.09.
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duration was lower (9 hours and 10 min), indicating an
insufficient amount of sleep. This suggests that objective
measures of lifestyle indicators are needed if we really
want to have an accurate picture.
Between-season and within-week variation in physical
activity, sedentary behaviour, sleep and fitness
Cardio-respiratory fitness fluctuated less within individuals
during a school year (highest ICC) compared to sedentary
time, physical activity and sleep duration, as might be
expected. Using accelerometers most studies find children
of approximately the same age as the present study to be
most physically active during the summer [7,8,33-37] or
(if summer is not measured) spring [38-41]; however, a
couple of studies found no seasonal variation [9,42], and
a decrease in outdoor activity has even been observed
during summer in a hot and humid area [43]. In the
current study, ICC from total physical activity was lower
(0.47 vs. 0.54 and 0.63 in boys and 0.86 in girls) and
MVPA intermediate (0.64 vs. 0.51 and 0.68 in boys and
0.72 in girls) compared to the two other studies that
have examined seasonal change in physical activity of
children using a repeated-measure design and reported
ICC [7,8]. Studies looking at seasonal changes in sedentary
time using accelerometers are sparse, inconsistent, and
cross-sectional of nature [34,35,42]. Two studies from the
UK in 5 and 7 year old children, respectively, found less
sedentary time during summer compared to the rest of
the year or more sedentary time during spring compared
to summer and autumn [34,35]. Finally, sedentary time
has been found not to be influenced by season of measurement [42]. In our study, it seems that sedentary time is
inversely related to MVPA. The same reliability (ICC) has
been reported previously, however, without reporting the
actual level of sedentary time [7]. Seasonal variations in
CRF vary widely from one study to another [44], and
could, in part, depend on the method used and the timing
of assessment. We observed a higher CRF during spring
compared to autumn, which is in good agreement with
the higher total physical activity and MVPA seen during
spring. However, as there was a tendency for children to
run longer in a second CRF test in the validation paper
(+15 m ≈ 0.5 ml/kg/min, P = 0.10; n = 47) [19], a learning
effect could partly explain the higher CRF during spring in
the present study, as it was the last test that was performed.
Furthermore, as children become 6 month older from
autumn to spring, the higher CRF during spring may
also partly be the result of increasing age rather than
seasonal variation. We found sleep duration to be shorter
during spring compared to autumn and winter. The same
was found in a cross-sectional study of 7 year-old children
using accelerometers; however that study indicated that
sleep duration was even lower during summer [45]. This
shorter sleep duration during spring has also been reported
Page 7 of 10
in university students [46] and under conditions of
temporal isolation, where subjects can choose their sleep
duration [47].
Given that hours of daylight and sunshine were lowest
and rainfall highest during the winter, followed by autumn
and spring, there is good reason to believe that the differences found in the present study could be caused by
the changing weather conditions of a northern European
country. As children were more physically active during
spring compared to autumn, our data suggest that rainfall
and hours of daylight and sunshine have a larger impact
on physical activity level compared to a modest change in
temperature. This is supported by a study showing that
regardless of season Canadian adolescents were found to
have 1-2% higher physical activity for every 10°C increase
in temperature, but 2-4% lower physical activity for every
10 mm of rainfall [48]. Furthermore, the most likely
explanation of the decreased sleep duration during spring
compared to autumn and winter is the longer photoperiod
at high northern or southern latitudes. Collectively, in the
present study, differences in lifestyle indicators between
seasons were found. Given that the weather conditions
change within seasons (although to a minor extent),
variation in lifestyle indicators within each season could be
anticipated as previously reported in Canadian adolescents
using physical activity recalls [48]. All lifestyle indicators
differed between weekdays and weekend days at all three
seasons (except sleep duration among girls). The lower
physical activity during weekends was especially evident
during winter. As winter in Denmark is characterised by
being cold, rainy and dark one might propose that physical
activity level of the children could benefit from providing
more indoor alternatives to the MVPA accumulated outdoor during the rest of the year.
The relatively high intra-individual variation could lead
to random error when a single measure (e.g. physical
activity) from a random time during the year is collected
to quantify a relationship with obesity or cardio-metabolic
risk. However, when repeated measurements of the exposure variable (e.g. physical activity) are collected, the ICC
which is a measure of reliability can be calculated and
used to adjust for this random error, hence obtaining
an improved estimate of the relationship with health
outcomes. Based on our calculated bias and MAPE, estimates from spring were the most inaccurate and biased
estimates of habitual lifestyle whereas autumn were the
most accurate and unbiased estimates of habitual physical
activity.
Correlates of physical of activity, sedentary behaviour,
sleep and fitness
Collectively, MVPA and sleep duration were lower and
sedentary time higher for older individuals. Given that the
need for sleep decreases from childhood to adulthood, this
Hjorth et al. BMC Public Health 2013, 13:808
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observation seems reasonable; however, as time spent in a
sedentary state was markedly higher in older individuals,
it is unclear whether this shorter sleep duration is due
to a decreased need or to changed behaviour of the
older children. Cardio-respiratory fitness was positively
associated with age, but negatively associated with pubertal
stage. As CRF is expressed per kg bodyweight and puberty
is a time of rapid weight gain, this negative association
seems reasonable. Finally, we speculated that the lifestyle
indicators could be different among children with different
ethnicity and education and found that, having two parents
born outside Denmark or with low education levels was
associated with shorter sleep duration and less time spent
in a sedentary state. As these children did not accumulate
more MVPA, data suggest that they accumulated more
light physical activity as they did not have less wear-time
(data not shown).
Connections between physical activity, sedentary
behaviour, sleep and fitness
Most of the correlations between the above variables were
rather weak (r < 0.20); however, total physical activity,
MVPA and sedentary time had moderate to high correlations (r between 0.49-0.91). The relatively strong correlations between these variables may partly be considered a
methodological matter, as they are a part of the same
24 hours, and partly a true observation. These variables
are difficult to separate from each other; however, our
negative correlations fit well with existing literature in
children [3]. Our negative correlation observed between
sleep duration and total physical activity (and MVPA) is
supported by a recent study that proposed the inherent
activity level to manifest itself in high activity level both
during the day and the night [49], which by definition
results in shorter accelerometer determined sleep duration.
Furthermore, the negative correlation between sleep
duration and screen time, but not between sleep duration
and sedentary time, could indicate that delayed sleep onset
was caused by extensive screen time in the later part of
the evening among certain individuals [50]. Finally, the
correlations became stronger between CRF and each of
sedentary time, total physical activity and MVPA when
including an average of the three seasons. Given that total
physical activity and in particular MVPA are expected to
correlate relatively closely with CRF, the increase from 8
to 25% of the explained variation between MVPA and
CRF indicate the strength of having multiple measures
during various seasons and including both weekdays and
weekend days.
Strength and limitations
In a school-based study it is often difficult to differentiate
the effect of school or school neighbourhood on physical
activity from the effect of season because different children
Page 8 of 10
and schools are included at different points in time. As
children from all schools were measured during all three
seasons our observed effect of season was independent of
school and school neighbourhood. A further strength of
the study is the assessment of repeated measures across
different seasons, which makes it possible to determine
ICC, bias, MAPE and minimise random error when
used as potential explanatory variables. Finally, this paper
introduce new standards by reporting the amount of time
spent being sedentary, active and sleeping in a large cohort
of children studied in their natural environment with the
use of objective measurements.
Limitations of the measure of physical activity used are
the inability to capture cycling and load-bearing activity as
well as swimming and other activities where the monitor
was removed. Bearing this in mind, and the fact that
the PA recommendations are based on studies using
self-reported data which tend to overestimate activity, the
number of children fulfilling the physical activity recommendations might be underestimated in this and other
studies that use accelerometers. As self-reported time
spent cycling during weekdays was lower during winter
(10 min/day) compared to autumn and spring (14 min/
day; P < 0.001) (data not shown) our observed differences
in physical activity between seasons are expected to be even
larger than reported. Time spent ≥1520 cpm have been
shown not to be influenced by epoch length in adolescents
and time spent ≥3450 to be underestimated in children
using 60 sec epochs compared to 5, 15 and 30 sec [51]. It
is, therefore, possible that MVPA (time spent ≥2296 cpm)
in our study is underestimated; however to compare with
previously conducted studies and to use validated cut-off
points, an epoch length of 60 sec was used from the vertical
axis only. We did not separately examine moderate PA and
vigorous PA as accelerometers have a limited capacity to
correctly classify these when using an epoch length of
60 sec [51]. No overall differences were observed if total
physical activity was expressed as 3-axis vector magnitude
and the seasonal and weekly variation in lifestyle indicators
were not affected by wear-time. In a recent pilot-study
using a similar population we tested the accordance
between waist- and wrist-worn devices and concluded
that waist-worn devices can provide a proxy measure of
sleep duration in epidemiologic studies [52]. The correlations and differences between seasons could, therefore, be
trusted; however, given that waist-worn devices tend to
overestimate sleep compared to wrist-worn devices [52],
the actual sleep duration in the present study might be
shorter than reported. Other things to bear in mind is that
the educational level of the parents was somewhat higher
than the average Danish population, and the fact that no
measurements were taken during the summer, since this
was a school based-study collecting data during normal
school weeks and not during vacations.
Hjorth et al. BMC Public Health 2013, 13:808
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Conclusions
Approximately two thirds of the sample exceeded the
screen time recommendation of <2 h per day and two
thirds did not fulfil the physical activity recommendation
of 60 min MVPA per day. Given that sedentary time was
higher and physical activity (including number of children
fulfilling physical activity recommendations) was lower
during winter and during weekends, these time-periods
might serve as key intervention periods in order to
promote a healthy lifestyle in countries at high northern
latitudes. The most accurate and unbiased estimates of
physical activity came from autumn; however children’s
sedentary time, physical activity, sleep duration and
CRF showed considerable intra-individual variation when
measured across three different seasons, suggesting that
a single measurement taken at one point in time may
not adequately provide an accurate assessment of these
lifestyle indicators in children.
Abbreviations
CI: Confidence interval; CPM: Counts/min; CRF: Cardio-respiratory fitness;
ICC: Intra-class correlation coefficient; MVPA: Moderate-to-vigorous physical
activity.
Page 9 of 10
5.
6.
7.
8.
9.
10.
11.
12.
13.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
MH coordinated the data collection, analysed and interpreted the data and
drafted the manuscript. AA, IT and KM were active in the planning of the
study and reviewed the article critically. AS were active in the planning of
the study, discussed the analysis and interpretation of the data and reviewed
the manuscript critically. JP discussed the analysis and interpretation of the
data and reviewed the manuscript critically. All authors read and approved
the final manuscript.
Acknowledgements
The study is part of the OPUS project ‘Optimal well-being, development and
health for Danish children through a healthy New Nordic Diet’ supported by
a grant from the Nordea Foundation. The authors are very grateful to the
participants and would also like to acknowledge the school staffs as well as
other researchers and staff in the OPUS project.
Author details
1
Department of Nutrition, Exercise and Sports, Faculty of Science, University
of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Copenhagen,
Denmark. 2Healthy Active Living and Obesity Research Group, Children’s
Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.
3
Division of Nutrition, DTU Food, National Food Institute, Technical University
of Denmark, Copenhagen, Denmark.
14.
15.
16.
17.
18.
19.
20.
21.
22.
Received: 25 January 2013 Accepted: 3 September 2013
Published: 8 September 2013
23.
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